Adaptive Real-Time Removal of Impulse Noise in Medical Images

HosseinKhani, Zohreh; Hajabdollahi, Mohsen; Karimi, Nader; Soroushmehr, S. M. Reza; Shirani, Shahram; Najarian, Kayvan; Samavi, Shadrokh

doi:10.1007/s10916-018-1074-7

Cited by 20 publications

(8 citation statements)

References 22 publications

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“…Furthermore, label noise is the common problem in medical image datasets. The label noise problem degrades the label quality of medical images [ 39 , 40 ], which will make the medical image mismatch with its real label, and have a negative effect in the development of DL. Manually filtering all the samples undoubtedly raises labor costs, and it is inefficient when dealing with large datasets.…”

Section: Discussionmentioning

confidence: 99%

Deep Learning Using CT Images to Grade Clear Cell Renal Cell Carcinoma: Development and Validation of a Prediction Model

Yang

Zhang

et al. 2022

Cancers

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This retrospective study aimed to develop and validate deep-learning-based models for grading clear cell renal cell carcinoma (ccRCC) patients. A cohort enrolling 706 patients (n = 706) with pathologically verified ccRCC was used in this study. A temporal split was applied to verify our models: the first 83.9% of the cases (years 2010–2017) for development and the last 16.1% (year 2018–2019) for validation (development cohort: n = 592; validation cohort: n = 114). Here, we demonstrated a deep learning(DL) framework initialized by a self-supervised pre-training method, developed with the addition of mixed loss strategy and sample reweighting to identify patients with high grade for ccRCC. Four types of DL networks were developed separately and further combined with different weights for better prediction. The single DL model achieved up to an area under curve (AUC) of 0.864 in the validation cohort, while the ensembled model yielded the best predictive performance with an AUC of 0.882. These findings confirms that our DL approach performs either favorably or comparably in terms of grade assessment of ccRCC with biopsies whilst enjoying the non-invasive and labor-saving property.

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Section: Discussionmentioning

confidence: 99%

Deep Learning Using CT Images to Grade Clear Cell Renal Cell Carcinoma: Development and Validation of a Prediction Model

Yang

Zhang

et al. 2022

Cancers

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“…Image processing is used in several fields such as face recognition scanning techniques, medical imaging, etc. Multiple types of noises like impulse noise are the sources of image corruption [1][2][3]. These noises are generated during transmission through a communication channel.…”

Section: Introductionmentioning

confidence: 99%

A New Adaptive Filter to Remove Impulsive Noise in Color Images

Atitallah

2022

IEEJ Transactions Elec Engng

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In this paper, a new adaptive vector directional distance filter (AVDDF) is proposed. The AVDDF filter is developed to remove the 'salt and papper' impulsive noise in color images. This algorithm consists in first step to detect pixels that are likely to have been contaminated with noise by using a threshold value. In second step, after recognizing the corrupted pixels, the smallest angular-magnitude distance is used to replace the noisy pixels. The proposed filter is tested with several standard color images which are contaminated with various levels of 'salt and pepper' impulsive noise (3%, 5%, 10%, 20% and 30%). The performance of the proposed filter is measured with peak signal-to-noise ratio (PSNR) and normalized color difference (NCD). The simulation results show that the proposed filter permits to ameliorate the performance of the classic vector directional distance filter (VDDF). Indeed, it provides an improvement by an average of 5% and 26% in the PSNR and NCD relative to the VDDF filter, respectively, with a small increase in the execution time by 6%. Besides, the AVDDF filter allows also an enhancement by an average of 2% and 14% in the PSNR and NCD relative to adaptive vector median filter (AVMF), respectively.

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“…Transmission, acquisition, image processing, and storage are the main causes of impulsive noise. The salt noise has an intensity of 255, whereas the pepper noise has a value of zero [7] [8]. Hence, impulse noise reduction is a crucial part of human dental X-Ray image diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Denoising of Impulse Noise using Partition-Supported Median, Interpolation and DWT in Dental X-Ray Images

Shajahan¹,

Aris²,

Usman³

et al. 2022

IJACSA

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The impulse noise often damages the human dental X-Ray images, leading to improper dental diagnosis. Hence, impulse noise removal in dental images is essential for a better subjective evaluation of human teeth. The existing denoising methods suffer from less restoration performance and less capacity to handle massive noise levels. This method suggests a novel denoising scheme called "Noise removal using Partition supported Median, Interpolation, and Discrete Wavelet Transform (NRPMID)" to address these issues. To effectively reduce the salt and pepper noise up to a range of 98.3 percent noise corruption, this method is applied over the surface of dental X-ray images based on techniques like mean filter, median filter, Bi-linear interpolation, Bi-Cubic interpolation, Lanczos interpolation, and Discrete Wavelet Transform (DWT). In terms of PSNR, IEF, and other metrics, the proposed noise removal algorithm greatly enhances the quality of dental X-ray images.

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Adaptive Real-Time Removal of Impulse Noise in Medical Images

Cited by 20 publications

References 22 publications

Deep Learning Using CT Images to Grade Clear Cell Renal Cell Carcinoma: Development and Validation of a Prediction Model

Deep Learning Using CT Images to Grade Clear Cell Renal Cell Carcinoma: Development and Validation of a Prediction Model

A New Adaptive Filter to Remove Impulsive Noise in Color Images

Denoising of Impulse Noise using Partition-Supported Median, Interpolation and DWT in Dental X-Ray Images

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